Lab 3: Intro to ggplot2
Anh Le (anh.le@duke.edu)
Most materials and inspiration come from the ggplot2 book by Hadley Wickham.
Introduction
# install.packages("ggplot2") if you need it
library("ggplot2")
library("gridExtra")This tutorial aims to teach you not only about the ggplot2 syntax but also a new way of thinking about graphics. What’s revolutionary about ggplot2 is really this “Grammar of Graphics” thinking (i.e. what gg- stands for).
Deconstructing a Scatterplot (learning about Aesthetics)
Below is a simple scatterplot. How exactly is data represented in a scatterplot?
df <- data.frame(x = c(1, 2, 4), y = c(1, 3, 2))
ggplot(data = df) + geom_point(aes(x, y)) + xlim(0, 5) + ylim(0, 5)
Each observation is represented by a point, whose x- and y- coordinate represent the values of the two variables. If I have to describe to you how to recreate the plot, I can just give you a list of 3 tuples, one tuple for each point (1, 1), (2, 3), (4, 2). This list is the essence of a scatterplot.
Along with the coordinates, each point / tuple can also have size, color, and shape.
These attribute are called aesthetics, and are the properties that can be perceived on the graphic. Each aesthetic can be mapped to a variable, or set to a constant value.
Q (conceptual): Without looking at the code, in this plot, what are the aesthetics, and what variables are they mapped to?
Q (ggplot syntax): Now look at the code. How did I specify the mapping?
Q (comparison with base R): How would you create the color plot in base R? Do you see how ggplot2 is less ad-hoc and more expressive?
data("mpg")
p1 <- ggplot(data = mpg, aes(x = displ, y = hwy)) +
geom_point() +
labs(y = "Mile per gallon", x = "Engine size")
p2 <- ggplot(data = mpg, aes(x = displ, y = hwy, color = factor(cyl))) +
geom_point() +
labs(y = "Mile per gallon", x = "Engine size")
p3 <- ggplot(data = mpg, aes(x = displ, y = hwy, shape = factor(cyl))) +
geom_point() +
labs(y = "Mile per gallon", x = "Engine size")
grid.arrange(p1, p2, p3, nrow = 3)
Note that the aesthetics is the essence of the graph – that’s all we need to map the data to the graphics. In the plot above, I use points as the geometric form (hence geom_point), but it doesn’t have to be. I can also use other geometric form, such as line, or bar (geom_line, geom_bar). Notice how the geomtric form changes, but the fundamental mapping of data to graphics does not change.
This is what we means by “grammar of graphics”. The line plot and bar plot are grammatically correct, even though it makes little sense.
p_point <- ggplot(data = mpg, aes(x = displ, y = hwy)) +
geom_point() +
labs(y = "Mile per gallon", x = "Engine size")
p_line <- ggplot(data = mpg, aes(x = displ, y = hwy)) +
geom_line() +
labs(y = "Mile per gallon", x = "Engine size")
p_bar <- ggplot(data = mpg, aes(x = displ, y = hwy)) +
geom_bar(stat = 'identity', position = "identity", color = 'red') +
labs(y = "Mile per gallon", x = "Engine size")
grid.arrange(p_point, p_line, p_bar, nrow=3)
Setting aesthetics to a constant
Sometimes I want to simply use a different color for my geoms, which is different from mapping the color to a variable. See:
p1 <- ggplot(data = mpg, aes(x = displ, y = hwy)) +
geom_point(color = 'red')
p2 <- ggplot(data = mpg, aes(x = displ, y = hwy)) +
geom_point(aes(color = factor(cyl)))
grid.arrange(p1, p2, nrow = 2)
Grouping
Q: Without looking at the code, what aes are specified in this plot?
The code that produces the line plot is below (notice the group aesthetics). What would be consequence of not specifying this aesthetics?
ggplot(Oxboys, aes(age, height, group = Subject)) +
geom_line() +
labs(title = "Height and Age of Oxford boys")ggplot(Oxboys, aes(age, height)) +
geom_line() +
labs(title = "Height and Age of Oxford boys")
More about geoms
There are many geom built into ggplot2 see this doc. With this wide variety of selection, you can build all the common plot types.
Q (common plot): Think about how different geom can be useful:
But the magic is really in building any custom plot that you want, mixing and matching different geoms.
ggplot(data = mpg, aes(displ, hwy, color = factor(cyl))) +
geom_point() +
geom_smooth(data= subset(mpg, cyl != 5), method="lm")
Building a plot layer by layer
As you may have noticed, the last plot has two geoms. In fact, we can add as many geoms as we want. This is thought of as building the plot layer by layer.
# First I add geom_point as the first layer
ggplot(data = mpg, aes(displ, hwy, color = factor(cyl))) +
geom_point()
# Then I add geom_smooth as the second layer
ggplot(data = mpg, aes(displ, hwy, color = factor(cyl))) +
geom_point() +
geom_smooth(data= subset(mpg, cyl != 5), method="lm")
A rather extreme example of layering:
Overriding default aesthetics mapping
Now that we know how to build the plot layer by layer, sometimes the situation demands that we have different aesthetics mapping for each layer.
For example, consider the Oxford boy dataset. This is panel dataset, and I want to build 2 layers: a boxplot layer for each occasion (i.e. variation across boys at each time point), and a line layer for each boy (i.e. variation over time of each boy).
ggplot(data = Oxboys, aes(Occasion, height)) +
geom_boxplot()
ggplot(data = Oxboys, aes(Occasion, height, group = Subject)) +
geom_line()
I can specify an aesthetic mapping specific to each layer as follows
ggplot(data = Oxboys) +
geom_boxplot(aes(Occasion, height)) +
geom_line(aes(Occasion, height, group = Subject), color = 'blue')
Programming with ggplot2
Like dplyr, ggplot2 belongs to a set of R packages called the tidyverse, whose philosophy is to make statistical programming in R as convenient as possible.
In ggplot2, it means that there are a lot of ways to reduce repitition in your code (i.e. the DRY principle - Don’t Repeat Yourself).
When ``zooming’’ in on the plot, it’s useful to use iteratively to quickly find the best view.
ggplot(data = diamonds) + geom_point(aes(x, y))
last_plot() + xlim(3, 11) + ylim(3, 11)## Warning: Removed 10 rows containing missing values (geom_point).
last_plot() + xlim(4, 10) + ylim(4, 10)## Scale for 'x' is already present. Adding another scale for 'x', which
## will replace the existing scale.## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.## Warning: Removed 478 rows containing missing values (geom_point).
last_plot() + xlim(4, 5) + ylim(4, 5)## Scale for 'x' is already present. Adding another scale for 'x', which
## will replace the existing scale.
## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.## Warning: Removed 36914 rows containing missing values (geom_point).
last_plot() + xlim(4, 4.5) + ylim(4, 4.5)## Scale for 'x' is already present. Adding another scale for 'x', which
## will replace the existing scale.
## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.## Warning: Removed 44681 rows containing missing values (geom_point).
Saving a scale to a variable makes it easy to apply exactly the same scale to multiple plots. You can do the same thing with layers and facets too.
gradient_rb <- scale_colour_gradient(low = "red", high = "blue")
ggplot(data = mpg) + geom_point(aes(cty, hwy, color = displ)) + gradient_rb
ggplot(data = msleep) + geom_point(aes(bodywt, brainwt, color = awake)) + gradient_rb## Warning: Removed 27 rows containing missing values (geom_point).
Creating a custom geom function saves typing when creating plots with similar (but not the same) components.
geom_lm <- function(formula = y ~ x) {
geom_smooth(formula = formula, se = FALSE, method = "lm")
}
ggplot(data = mtcars, aes(mpg, wt)) + geom_point() + geom_lm()
library(splines)
ggplot(data = mtcars, aes(mpg, wt)) + geom_point() + geom_lm(y ~ ns(x, 3))
Other extensions to ggplot2
Since programming with ggplot2 is easy, a lot of people have contributed their own extensions.
See full list here: http://www.ggplot2-exts.org/gallery/
My favorites: - https://github.com/jrnold/ggthemes : custom made themes for ggplot2, e.g. Stata, Excel - https://github.com/dgrtwo/gganimate : animated graph - https://github.com/slowkow/ggrepel : automatically un-overlap text labels